[jira] [Assigned] (ARROW-9974) [Python][C++] pyarrow version 1.0.1 throws Out Of Memory exception while reading large number of files using ParquetDataset

["Ben Kietzman (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-9974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ben Kietzman reassigned ARROW-9974:
-----------------------------------

    Assignee: Ben Kietzman

> [Python][C++] pyarrow version 1.0.1 throws Out Of Memory exception while reading large number of files using ParquetDataset
> ---------------------------------------------------------------------------------------------------------------------------
>
>                 Key: ARROW-9974
>                 URL: https://issues.apache.org/jira/browse/ARROW-9974
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++, Python
>            Reporter: Ashish Gupta
>            Assignee: Ben Kietzman
>            Priority: Critical
>              Labels: dataset
>             Fix For: 2.0.0
>
>         Attachments: legacy_false.txt, legacy_true.txt
>
>
> [https://stackoverflow.com/questions/63792849/pyarrow-version-1-0-bug-throws-out-of-memory-exception-while-reading-large-numbe]
> I have a dataframe split and stored in more than 5000 files. I use ParquetDataset(fnames).read() to load all files. I updated the pyarrow to latest version 1.0.1 from 0.13.0 and it has started throwing "OSError: Out of memory: malloc of size 131072 failed". The same code on the same machine still works with older version. My machine has 256Gb memory way more than enough to load the data which requires < 10Gb. You can use below code to generate the issue on your side.
> {code}
> import pandas as pd
> import numpy as np
> import pyarrow.parquet as pq
> def generate():
>     # create a big dataframe
>     df = pd.DataFrame({'A': np.arange(50000000)})
>     df['F1'] = np.random.randn(50000000) * 100
>     df['F2'] = np.random.randn(50000000) * 100
>     df['F3'] = np.random.randn(50000000) * 100
>     df['F4'] = np.random.randn(50000000) * 100
>     df['F5'] = np.random.randn(50000000) * 100
>     df['F6'] = np.random.randn(50000000) * 100
>     df['F7'] = np.random.randn(50000000) * 100
>     df['F8'] = np.random.randn(50000000) * 100
>     df['F9'] = 'ABCDEFGH'
>     df['F10'] = 'ABCDEFGH'
>     df['F11'] = 'ABCDEFGH'
>     df['F12'] = 'ABCDEFGH01234'
>     df['F13'] = 'ABCDEFGH01234'
>     df['F14'] = 'ABCDEFGH01234'
>     df['F15'] = 'ABCDEFGH01234567'
>     df['F16'] = 'ABCDEFGH01234567'
>     df['F17'] = 'ABCDEFGH01234567'
>     # split and save data to 5000 files
>     for i in range(5000):
>         df.iloc[i*10000:(i+1)*10000].to_parquet(f'{i}.parquet', index=False)
> def read_works():
>     # below code works to read
>     df = []
>     for i in range(5000):
>         df.append(pd.read_parquet(f'{i}.parquet'))
>     df = pd.concat(df)
> def read_errors():
>     # below code crashes with memory error in pyarrow 1.0/1.0.1 (works fine with version 0.13.0)
>     # tried use_legacy_dataset=False, same issue
>     fnames = []
>     for i in range(5000):
>         fnames.append(f'{i}.parquet')
>     len(fnames)
>     df = pq.ParquetDataset(fnames).read(use_threads=False)
>  
>  {code}



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